While robotic perception has advanced rapidly in vision and touch, enabling robots to reason about indoor fungal contamination from weak, diffusion-dominated chemical signals remains an open challenge. We introduce Scensory, a learning-based robotic olfaction framework that simultaneously identifies fungal species and localizes their source from short time series measured by affordable, cross-sensitive VOC sensor arrays. Temporal VOC dynamics encode both chemical and spatial signatures, which we decode through neural networks trained on robot-automated data collection with spatial supervision. Across five fungal species, Scensory achieves up to 89.85% species accuracy and 87.31% source localization accuracy under ambient conditions with 3-7s sensor inputs. These results demonstrate real-time, spatially grounded perception from diffusion-dominated chemical signals, enabling scalable and low-cost source localization for robotic indoor environmental monitoring.

翻译：尽管机器人感知在视觉和触觉领域已取得显著进展，但如何使机器人通过弱扩散主导的化学信号推理室内真菌污染仍是一项开放挑战。我们提出Scensory——一种基于学习的机器人嗅觉框架，该框架能通过低成本交叉敏感VOC传感器阵列测量的短时序信号，同步实现真菌物种识别与源定位。时间序列VOC动态同时编码了化学与空间特征，我们通过基于机器人自主数据采集及空间监督训练的神经网络对其进行解码。在五种真菌物种的实验中，Scensory在3-7秒传感器输入的环境条件下实现了高达89.85%的物种识别准确率与87.31%的源定位准确率。这些结果表明，基于扩散主导化学信号的实时空间感知是可行的，为机器人的室内环境监测提供了可扩展、低成本的源定位方案。